Association between hospital accreditation and healthcare providers’ perceptions of patient safety culture: a longitudinal study in a healthcare network in Brazil
Bibliographic record
Abstract
Abstract Background Enhancing security and dependability of health systems necessitates resource allocation, a well-defined infrastructure, and a steadfast commitment to ensuring its safety and stability over time. This study aimed to assess changes in patient safety culture over time (2014–2022) within a network of private hospitals in Brazil and to examine its association with the hospital accreditation process. The study utilized the Hospital Survey on Patient Safety Culture (HSOPSC) to measure healthcare professionals’ perceptions of patient safety culture. Methods The HSOPSC questionnaire was distributed to 71 hospitals between 2014 and 2022 with 259,268 responders. Hospitals were classified as accredited (AH) or non-accredited (NAH). A linear mixed-effects regression model was used to analyze the trend of dimension scores over time, accounting for both fixed and random effects to accommodate within-hospital correlations and variations across time points. Results Out of 12 dimensions analysed, 11 significantly improved, and one (“frequency of reported events”) remained unchanged over time (p = 0.84). Two dimensions had < 50% positive responses: “communication openness” (47.13% [38.19–58.73]) and “nonpunitive response to errors” (41.24% [34.13–51.98]). Safety culture improved among AH across all, but “frequency of reported events” (p = 0.12), dimensions. Among NAH, “frequency of reported events” decreased over time (p = 0.008) while other dimensions remained unchanged. Conclusion Our results suggest an improvement in patient safety culture within this network of private hospitals in Brazil from 2014 to 2022. While accreditation appears to be associated with fostering a culture of safety over time, our study does not establish a causal relationship. Additionally, non-accredited hospitals tended to report fewer adverse events, which may indicate underreporting and missed opportunities for healthcare system improvement through adverse event analysis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".